code: def forward(self, data):
      x, edge_index, batch = data.x, data.edge_index, data.batch
      x = self.conv1(x, edge_index)
      for conv in self.convs:
          x = conv(x, edge_index)
      x = global_mean_pool(x, batch)
      x = F.relu(self.lin1(x))
      x = F.dropout(x, p=0.5, training=self.training)
      x = self.lin2(x)
      return F.log_softmax(x, dim=-1)
  
imports: import torch
  import torch.nn.functional
  from torch.nn import BatchNorm1d
  from torch.nn import Linear, ReLU, Sequential
  from torch_geometric.nn import GINConv, JumpingKnowledge, global_mean_pool
label_desc: Use image node_id 3 for calling the GIN obj's underlying member method code with example usage: obj.forward(data) and returns: F